Social media generally involves large number of users that interact socially with one another and in which such users can freely express and share opinions with other users via a social networking application. Social media encompasses online media such as, for example, collaborative projects (e.g. Wikipedia), blogs and microblogs (e.g. Twitter), content communities (e.g. YouTube), social networking sites (e.g. Facebook), virtual game worlds (e.g. World of Warcraft), and virtual social worlds (e.g. Second Life). Online social media can be harvested to generate data regarding products, services, brands, competition, and industries and to actively influence a purchase decision. In the marketing domain, a buyer having a greater impact on other buyers may be treated as a powerful promoter and/or an influential user, and their opinions may thus determine the marketing acceptance of a certain product.
Enterprise Marketing Services (EMS) delivers personalized content to a broad customer base in accordance with particular user profile information with the immediate goal of improving the response rate. Such services, however, do not take advantage of social network data to effectively target the influential user in a social network context and to leverage substantial social influence regarding product purchasing. Social media marketing, which employs social network data to benefit the enterprise and an individual with additional marketing channel, has recently gained more traction. In order to optimize the coverage of the marketing messages for the enterprise, it is essential to select a set of influential users in the social network (i.e., powerful promoters). Such influential users do not necessarily have the most followers, but possess the ability to influence other people's decision making in the purchase process.
The majority of academic research and commercial social media listening tools (e.g. Radian6, Sysomos, Klout) provide algorithms that score and rank a user's influences in a social network. Such influence scoring methods generate a single score value with little topical insights and topical context information. The influence scores are usually computed based on a network structure metric (e.g., number of followers or page rank) or an activity-centric metric in the social network (e.g., frequency of posts, responses, number of likes or comments within user's direct connections, etc.). Such approaches do not take into account a temporal diffusion model in any topical context.
Furthermore, the usage of social media listening tools to benefit an enterprise and individuals with additional marketing channel ignores user interests. For example, prior art approaches to date are only capable of targeting general influential users without any topical context for a specific product marketing campaign. Ignoring user interests and topics, however, can seriously affect the likelihood of the message adoption by the targeted users. Unfortunately, it is difficult to relate the importance of scores to the marketing metrics (e.g. response rate, coverage or reach, the number of positive feedbacks, and etc.) and the temporal diffusion information is not considered into the static social graph computing.
Based on forgoing, it is believed that a need exists for an improved system and method identifying a key influencer in a social media for an enterprise marketing service utilizing a topic modeling and a social diffusion analysis. A need also exists for an improved method for predicting a marketing campaign message propagation speed and coverage in the social network, as will be described in greater detail herein.